CppCon 2026

For a long time, programming was constrained by our ability to produce code. That constraint is eroding rapidly. AI systems can now generate substantial amounts of code with little effort, while inspection, validation, and integration remain difficult.

This transition alters the economics of software engineering. Code is becoming easier to generate than to reason about, and sound judgment grows scarcer relative to specialized intelligence. The emphasis shifts from authoring programs to understanding systems: their structure, invariants, interactions, and long-term evolution.

The talk examines this transition through the lenses of history, language design, and recent developments in AI and metaprogramming. It argues that the central challenges ahead concern not code generation, but the management of complexity at scale.

Topics include: - historical parallels and failed predictions in computing - AI-assisted software development - language design and metaprogramming - C++ reflection and compile-time programming - abstraction as context compression - complexity management in large systems

AI will undoubtedly transform how we build software. As before, the decisive factor will be our capacity to adapt.